Address:
Campus SophiaTech
450 Route des Chappes
06410 Biot, FRANCE
Office: 419
Phone: +33 4 93 00 81 35
Email Address: maurizio.filippone [at] eurecom.fr
Book time with me on: youcanbook.me
NEWS
25-07-23 Poster presentation at ICML of our paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes"
23-07-23 Delighted to be an invited speaker at the AABI workshop at ICML, presenting "Bayesian Autoencoders"
30-05-23 Check out our new paper "One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models" (link)
29-05-23 I'm visiting NTNU University this week, acting as opponent for a Ph.D. thesis and presenting "Functional Priors for Bayesian Deep Learning"
29-05-23 I'm visiting CTU and the UTIA Institute in Prague this week, presenting "Bayesian Deep Learning"
25-04-23 The paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes" has been accepted at ICML 2023! (pdf)
29-03-23 The paper "How Much is Enough? A Study on Diffusion Times in Score-based Generative Models" has been accepted in the Entropy Journal! (link)
01-03-23 Check out our new paper "Continuous-Time Functional Diffusion Processes" - joint work with M. Heinonen at Aalto (link)
09-02-23 Check out our new paper "Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes" - joint work with S. Mandt and B. Shahbaba at UCI (link)
18-01-23 I'm visiting Aalto University this week, acting as opponent for a Ph.D. thesis and presenting Functional priors for Bayesian Deep Learning
06-12-22 Webinar with the discussion of the Bayesian Analysis paper "Deep Gaussian Processes for Calibration of Computer Models". (link)
30-11-22 Poster presentation at NeurIPS of our JMLR paper "All You Need is a Good Functional Prior for Bayesian Deep Learning" (link)
24-10-22 Talk at the workshop on Statistical Deep Learning Functional priors for Bayesian deep learning (link)
17-10-22 I will visit Data61 in Sydney and University of Wollongong over the next two weeks.
15-08-22 Participating in the Dagstuhl seminar Differential Equations and Continuous-Time Deep Learning organized by M. Welling, D. Duvenaud, M. Heinonen and M. Tiemann (link)
21-06-22 The paper "Deep Gaussian Processes for Calibration of Computer Models" has been selected to be a discussion paper! (pdf)
15-05-22 The paper "Revisiting the Effects of Stochasticity for Hamiltonian Samplers" has been accepted at ICML 2022! (pdf)
25-03-22 The paper "All You Need is a Good Functional Prior for Bayesian Deep Learning" will appear in JMLR! (pdf)
24-10-21 Participating in the Dagstuhl seminar Probabilistic Numerical Methods – From Theory to Implementation organized by P. Hennig, I. Ipsen, M. Mahsereci, T. Sullivan (link)
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